AbstractAfter a general discussion about convergence and convergence rates for regularization methods in Banach spaces, we present a general method that can be used to modify regularization methods in L2 in such a way that uniform convergence, which is often preferred in concrete applications to just L2-convergence, is obtained. We prove results about convergence rates in the uniform norm and discuss questions of parameter choice. The theoretical results will be supported by numerical examples, which indicate that although the results are asymptotic in character, they are of some relevance also for actual computations
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle ...
International audienceIn this work, we show that the regularization methods based on filter function...
AbstractAfter a general discussion about convergence and convergence rates for regularization method...
Abstract In the recent past the authors, with collaborators, have published convergence rate results...
There exists a vast literature on convergence rates results for Tikhonov regularized minimizers. We ...
Tikhonov-type regularization of linear and nonlinear ill-posed problems in abstract spaces under spa...
Tikhonov-type regularization of linear and nonlinear ill-posed problems in abstract spaces under spa...
We study the application of the Augmented Lagrangian Method to the solution of linear ill-posed prob...
We consider generalized inverses and linear ill-posed problems in Banach spaces, and the concept of ...
In this paper we analyze two regularization methods for nonlinear ill-posed problems. The first is a...
In recent years, a series of convergence rates conditions for regulariza-tion methods has been devel...
Chen, Dehan.Thesis Ph.D. Chinese University of Hong Kong 2016.Includes bibliographical references (l...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle ...
International audienceIn this work, we show that the regularization methods based on filter function...
AbstractAfter a general discussion about convergence and convergence rates for regularization method...
Abstract In the recent past the authors, with collaborators, have published convergence rate results...
There exists a vast literature on convergence rates results for Tikhonov regularized minimizers. We ...
Tikhonov-type regularization of linear and nonlinear ill-posed problems in abstract spaces under spa...
Tikhonov-type regularization of linear and nonlinear ill-posed problems in abstract spaces under spa...
We study the application of the Augmented Lagrangian Method to the solution of linear ill-posed prob...
We consider generalized inverses and linear ill-posed problems in Banach spaces, and the concept of ...
In this paper we analyze two regularization methods for nonlinear ill-posed problems. The first is a...
In recent years, a series of convergence rates conditions for regulariza-tion methods has been devel...
Chen, Dehan.Thesis Ph.D. Chinese University of Hong Kong 2016.Includes bibliographical references (l...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
In the last decade l1-regularization became a powerful and popular tool for the regularization of In...
Regularization methods aimed at finding stable approximate solutions are a necessary tool to tackle ...
International audienceIn this work, we show that the regularization methods based on filter function...